Relationship between Alexithymia and latent trigger points in the upper Trapezius
Bibliographic record
Abstract
BACKGROUND: Latent trigger points (LTrPs) can be activated by future events, leading to pain. Few studies have reported LTrP risk factors. It has been suggested that alexithymia is associated with myofascial pain and diminished awareness of physical sensation. This study was designed to evaluate the relation between alexithymia and LTrPs found the upper trapezius of healthy individuals. METHODS: The correlation between LTrPs and alexithymia, and between LTrPs and depression was analyzed in 160 healthy participants (80 male, mean age: 40.5 years [20 to 66 years]). Each participant was evaluated for potential LTrPs by careful manual examination and completed the Toronto Alexithymia Scale-20 (TAS-20) and the Beck Depression Inventory (BDI) to assess potential alexithymia and depressive symptoms, respectively. RESULTS: = 0.451). The LTrP risk for alexithymia was 2.74 (95% confidence interval [95% CI]: 2.10-3.58). There was no correlation between the TAS-20 and BDI scores (correlation coefficient: -0.04). Significant risk factors associated with LTrPs included the TAS-20 score (odds ratio [OR]: 1.11, 95% CI: 1.07-1.15) and age (OR: 1.05, 95% CI: 1.01-1.09). CONCLUSIONS: Alexithymia was associated with LTrPs in the upper trapezius of healthy individuals, suggesting that it may serve as a useful predictive factor. TRIAL REGISTRATION: UMIN000027468. Registered 23 May 2017(retrospectively registered).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".